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A Novel Adaptive-Boost-Based Strategy for Combining Classifiers Using Diversity Concept
Melbourne, Australia July 11-July 13
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICIS.2007.376th IEEE/ACIS International Conferenc ...
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Abbas Golestani, Kushan Ahmadian, Ali Amiri, MohammadReza JahedMotlagh, "A Novel Adaptive-Boost-Based Strategy for Combining Classifiers Using Diversity Concept," Computer and Information Science, ACIS International Conference on, pp. 128-134, 6th IEEE/ACIS International Conference on Computer and Information Science (ICIS 2007), 2007.
 
 
BibTex
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@article{ 10.1109/ICIS.2007.37,
author = {Abbas Golestani and Kushan Ahmadian and Ali Amiri and MohammadReza JahedMotlagh},
title = {A Novel Adaptive-Boost-Based Strategy for Combining Classifiers Using Diversity Concept},
journal ={Computer and Information Science, ACIS International Conference on},
volume = {0},
year = {2007},
isbn = {0-7695-2841-4},
pages = {128-134},
doi = {http://doi.ieeecomputersociety.org/10.1109/ICIS.2007.37},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - Computer and Information Science, ACIS International Conference on
TI - A Novel Adaptive-Boost-Based Strategy for Combining Classifiers Using Diversity Concept
SN - 0-7695-2841-4
SP128
EP134
A1 - Abbas Golestani,
A1 - Kushan Ahmadian,
A1 - Ali Amiri,
A1 - MohammadReza JahedMotlagh,
PY - 2007
KW - null
VL - 0
JA - Computer and Information Science, ACIS International Conference on
ER -
 
Abbas Golestani, Islamic Azad University; Iran University of Science and Technology, Iran
Kushan Ahmadian, Iran University of Science and Technology, Iran
Ali Amiri, Islamic Azad University; Iran University of Science and Technology, Iran
MohammadReza JahedMotlagh, Iran University of Science and Technology, Iran
In classifiers combination, the diversity rate among classifier?s outputs is one of the most important discussions. There are different methods for combining classifiers. AdaBoost is an incremental method for creating a classifiers ensemble in which every AdaBoost algorithm has a local centrality. It means that classifiers are data biased and classify special data. In this paper we intend to find a new method for combining classifiers by using AdaBoost method and diversity concept. We have checked this method over different data sets and compared results of this method with others. These results indicate that we can develop other versions of this method for achieving a better performance.
Citation:
Abbas Golestani, Kushan Ahmadian, Ali Amiri, MohammadReza JahedMotlagh, "A Novel Adaptive-Boost-Based Strategy for Combining Classifiers Using Diversity Concept," icis, pp.128-134, 6th IEEE/ACIS International Conference on Computer and Information Science (ICIS 2007), 2007
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